Establishing an Earth observation Super-resolution and Validation Framework for Improved Climate Hazard Assessment and Response in Forestry

The EMERALD project addresses critical challenges in enhancing forest resilience to climate-driven natural hazards, with a particular focus on the timely detection and monitoring of forest disturbances such as e.g. windthrows. These disturbances are increasingly amplified by climate extremes and pose substantial ecological and economic risks, including biodiversity loss, carbon stock degradation, and cascading impacts on ecosystem services. Despite the growing availability of Earth observation (EO) data, operational forest monitoring remains constrained by cloud cover, terrain-induced shadows, and limited spatial resolution, reducing the reliability of hazard assessment and early response.
To overcome these limitations, EMERALD extends SAFIR’s de-clouding and de-shadowing core capabilities and introduces super-resolution methods to enhance the spatial resolution of Sentinel-2 data.  More specifically, EMERALD introduces a latent super-resolution approach, in which high-resolution representations are not generated as an end product but as intermediate feature states optimized for downstream hazard-relevant tasks, such as forest disturbance detection, tree species discrimination, and health assessment. The super-resolution component is therefore task-supervised, coupling image reconstruction objectives with performance metrics from downstream applications to ensure that enhanced spatial detail directly translates into improved hazard assessment capability rather than purely visual fidelity.
A third core component of EMERALD is the rigorous validation of AI-derived products using high-quality image pairs combining Sentinel-2 observations with very high-resolution Uncrewed Aerial Vehicles (UAV) data for validation purposes. These paired datasets enable quantitative assessment of reconstruction fidelity, uncertainty, and disturbance detectability across spatial scales, strengthening confidence in AI outputs for decision makers. By leveraging datasets from diverse European forest landscapes ranging from Austria to Portugal, EMERALD explicitly addresses geographic transferability and bias, a critical requirement for continental-scale hazard and resilience monitoring.
By improving the accuracy, timeliness, and transparency of forest disturbance detection, EMERALD supports AI-enabled decision-making for forest managers and policymakers, demonstrating how advanced digital technologies can enhance resilience to climate-driven natural hazards.